{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers import AveragePooling1D\n",
    "from keras.utils import np_utils\n",
    "from keras.optimizers import Adam\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from contextlib import redirect_stdout\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n",
    "def get_data():\n",
    "    data = pd.read_csv(\"/kaggle/input/train-cnn/p2pData.txt\", sep=\" \", header=None)\n",
    "    label = pd.read_csv(\"/kaggle/input/train-cnn/p2pLabel.txt\", sep=\" \", header=None)\n",
    "    data_columns = data.shape[1]\n",
    "\n",
    "    for i in range(0, data_columns):   \n",
    "        if data[i][0] != data[i][0]:\n",
    "            del data[i]\n",
    "\n",
    "    data_columns = data.shape[1]\n",
    "    data.columns = np.arange(0, data_columns)\n",
    "\n",
    "    return data, label\n",
    "\n",
    "\n",
    "data, label = get_data()\n",
    "#data = data[:5000]\n",
    "#label = label[:5000]\n",
    "selectedFeatureList=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "data=data[selectedFeatureList]\n",
    "data_columns = data.shape[1]\n",
    "data.columns = np.arange(0, data_columns)\n",
    "\n",
    "data.describe().to_excel(\"./data_description_before.xlsx\")\n",
    "# forward-fill\n",
    "data = data.fillna(method=\"ffill\", axis=0)\n",
    "data.describe().to_excel(\"./data_description_after.xlsx\")\n",
    "\n",
    "scaler=StandardScaler()\n",
    "data=scaler.fit_transform(data)\n",
    "\n",
    "data=np.array(data)\n",
    "label=np.array(label)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, label, test_size=0.2)\n",
    "n_samples, n_features = X_train.shape\n",
    "\n",
    "\n",
    "def toOneHot(label):\n",
    "    enc = OneHotEncoder()\n",
    "    enc.fit(label)\n",
    "    label = enc.transform(label).toarray()\n",
    "    return label\n",
    "\n",
    "\n",
    "y_train = toOneHot(y_train)\n",
    "y_test = toOneHot(y_test)\n",
    "\n",
    "\n",
    "def create_model():\n",
    "\n",
    "    adam = Adam(lr=0.004,\n",
    "                beta_1=0.9,\n",
    "                beta_2=0.999,\n",
    "                epsilon=1e-08,\n",
    "                amsgrad=True)\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Dense(512, activation='relu', input_shape=(n_features, )))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(512, activation='relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(10, activation='relu'))\n",
    "    model.add(Dense(5))\n",
    "    model.add(Activation('softmax'))\n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer=adam,\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "model = create_model()\n",
    "\n",
    "with open('Model_Architecture.txt', 'w') as f:\n",
    "    with redirect_stdout(f):\n",
    "        model.summary()\n",
    "\n",
    "callbacks = [\n",
    "    EarlyStopping(monitor='val_acc',\n",
    "                  min_delta=0.00001,\n",
    "                  patience=6,\n",
    "                  verbose=0,\n",
    "                  mode='max',\n",
    "                  restore_best_weights=False),\n",
    "    ModelCheckpoint(filepath='./best_model.h5',\n",
    "                    monitor='val_acc',\n",
    "                    mode=\"max\",\n",
    "                    save_weights_only=False,\n",
    "                    save_best_only=True)\n",
    "]\n",
    "\n",
    "history = model.fit(X_train,\n",
    "                    y_train,\n",
    "                    batch_size=120,\n",
    "                    epochs=160,\n",
    "                    verbose=0,\n",
    "                    validation_split=0.2,\n",
    "                    callbacks=callbacks)\n",
    "\n",
    "#model.save(\"./best_model.h5\")\n",
    "\n",
    "training_score, training_accuracy = model.evaluate(X_train, y_train, verbose=0)\n",
    "test_score, test_accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
    "with open('output.txt', 'w') as f:\n",
    "    with redirect_stdout(f):\n",
    "        print('Training score:', training_score)\n",
    "        print('Training accuracy:', training_accuracy)\n",
    "        print('Test score:', test_score)\n",
    "        print('Test accuracy:', test_accuracy)\n",
    "\n",
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='val')\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.title(\"Loss\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.savefig(\"./loss.png\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history.history['accuracy'], label='train')\n",
    "plt.plot(history.history['val_accuracy'], label='val')\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.title(\"Accuracy\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.savefig(\"./accuracy.png\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
